






'''simple docstring'''



import re
import string

import numpy as np

import datasets


SCREAMING_SNAKE_CASE							=						'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

SCREAMING_SNAKE_CASE							=						'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

SCREAMING_SNAKE_CASE							=						'\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,							_KWARGS_DESCRIPTION					)
class UpperCAmelCase_       (     datasets.Metric					):




      """simple docstring"""




      def 					A__   (			self      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       return datasets.MetricInfo(
           description=_DESCRIPTION  ,   citation=_CITATION  ,   inputs_description=_KWARGS_DESCRIPTION  ,   features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string'''  ,   id='''sequence'''		),
                   '''references''': datasets.Value('''string'''  ,   id='''sequence'''		),
               }		)  ,   reference_urls=[]  ,   )






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		int=False  ,   UpperCAmelCase      :		Tuple=False  ,   UpperCAmelCase      :		List[str]=False  ,   )	->	Dict:


       '''simple docstring'''

       if regexes_to_ignore is not None:
        for s in regexes_to_ignore:
         lowercase       : str						=np.array([re.sub(UpperCAmelCase  ,   ''''''  ,   UpperCAmelCase		) for x in predictions]		)
         lowercase       : List[Any]						=np.array([re.sub(UpperCAmelCase  ,   ''''''  ,   UpperCAmelCase		) for x in references]		)
       else:
        lowercase       : int						=np.asarray(UpperCAmelCase		)
        lowercase       : str						=np.asarray(UpperCAmelCase		)

       if ignore_case:
        lowercase       : Optional[int]						=np.char.lower(UpperCAmelCase		)
        lowercase       : int						=np.char.lower(UpperCAmelCase		)

       if ignore_punctuation:
        lowercase       : str						=string.punctuation.maketrans(''''''  ,   ''''''  ,   string.punctuation		)
        lowercase       : int						=np.char.translate(UpperCAmelCase  ,   table=UpperCAmelCase		)
        lowercase       : Union[str, Any]						=np.char.translate(UpperCAmelCase  ,   table=UpperCAmelCase		)

       if ignore_numbers:
        lowercase       : int						=string.digits.maketrans(''''''  ,   ''''''  ,   string.digits		)
        lowercase       : List[Any]						=np.char.translate(UpperCAmelCase  ,   table=UpperCAmelCase		)
        lowercase       : int						=np.char.translate(UpperCAmelCase  ,   table=UpperCAmelCase		)

       lowercase       : List[Any]						=predictions == references

       return {"exact_match": np.mean(UpperCAmelCase		) * 100}




"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bool:




						'''simple docstring'''



						return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()


